Top Tech Jobs & Startup Jobs in Calgary

Owns the end-to-end development of iOS monetization experiences, including paywalls, pricing, subscriptions, upgrades, and purchases across global storefronts. Partners with product, design, analytics, finance, and legal to prototype, experiment, launch, and optimize reliable consumer-scale features. Responsibilities include architecture, performance optimization, staged rollouts, code reviews, mentoring, and advancing AI-native engineering practices.

Build and maintain premium mobile features, subscription and in-app purchase flows, paywalls, pricing experiences, and advertising functionality for Life360’s consumer app. Own feature quality, performance, reliability, experimentation, and incident response at 100M+ user scale. Collaborate with product, design, data science, and analytics teams; use AI coding agents extensively; contribute to architecture and code reviews; mentor engineers; and help evolve AI-native engineering practices.

Build and own Android growth and monetization experiments across subscriptions, pricing, advertising, purchases, and upgrade flows. Partner with product, design, data science, and analytics to instrument experiments, manage staged rollouts, monitor performance, and act on results. Use AI tools throughout development, contribute to Android architecture and engineering practices, participate in on-call support, mentor engineers, and help scale reliable mobile experiences to tens of millions of users.

Own revenue-focused data science from opportunity sizing through production ML. Investigate and measure product changes, build and operate batch and online ML systems for personalization, experimentation, and automation, and partner cross-functionally to integrate models into user-facing products. Establish monitoring, lineage, reproducibility, and security practices; improve data pipelines; mentor data scientists; and define ML development best practices while using AI tools to accelerate delivery.

Build and own Android growth and monetization experiments across subscriptions, advertising, purchases, and upgrade flows. Partner with product, design, data science, and analytics to instrument experiments, evaluate results, and ship reliable experiences at consumer scale. Use AI-native workflows for specification, coding, testing, and review; improve mobile engineering practices; contribute to architecture and code reviews; participate in on-call; and mentor engineers.

Design and run high-volume experiments across subscription and monetization experiences, including pricing, trial conversion, SKU routing, churn prevention, and win-back. Translate ambiguous briefs into testable hypotheses, collaborate with product, engineering, data science, and content teams, define success metrics, interpret results, and present learnings. The role requires strong visual and interaction design craft, animation skills, organized Figma files, proactive ideation, and effective use of AI and experimentation tools.

Leads MAU analytics and data science, managing decision scientists and data scientists while setting quality standards. Owns MAU driver modeling, forecasting, causal analysis, experimentation, and exploratory work supporting growth strategy. Establishes consistent analytical practices, partners with revenue, product, engineering, and data engineering leaders, and embeds the team in roadmap planning. Promotes AI-native workflows through coding agents, automation, and analytical tools while coaching team members into strategic advisors.

Design, implement, and own a reusable on-device ML inference platform for resource-constrained devices. Integrate inference into RTOS firmware, develop drivers and DMA paths, debug on real hardware, train and optimize models for microcontroller-class hardware, and ship features to production while raising team expertise and using AI-assisted tooling.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.